Computers are “starting to open their eyes,” said a senior fellow at Google.

"Microsoft researchers on Thursday announced a major advance in technology designed to identify the objects in a photograph or video, showcasing a system whose accuracy meets and sometimes exceeds human-level … "

2015 was a turning point year for machine learning.  Check it out: https://www.youtube.com/watch?v=8BFzu9m52sc Based on Andrej Karpathy's "NeuralTalk2" code, as modified by Kyle McDonald: a Brooklyn-based artist who "works in code".  Neuraltalk uses GPUs - another breakthrough for 2015, where my (and probably your!) laptop took a big leap in performance.

More and more organizations are realizing the tremendous benefit of machine learning to their bottom line, yet many are not ready to hire a full-time machine learning expert. So a machine learning contractor/consultant/freelancer makes sense. For instance, your business might depend upon "just in time" delivery of some goods, and you need to predict when…

Like any maturing discipline, machine learning is splitting into specialties.   And just as a surgeon uses a scalpel, and a general practitioner prefers a stethoscope, different tools are appropriate for different use cases within these subfields. In the last few months, I've run projects that have used tools in at least five categories.    Here's a survey"

It's critical that you understand machine learning, even if just a little bit. Why? Machine learning is at the heart of the most common artificial intelligence systems today. It's an important new technology that's moved beyond hype to the brink of an exponential explosion..."

Looking backwards, we’ve already been through the AI/ML hype cycle (remember The Fifth Generation of the 1980s?).  This second time around, it’s serious.  From this point of view, I disagree with where Gartner places Machine Learning on its 2015 emerging technology curve, as shown in the graphic above.  We’re not just past the “Peak of Inflated Expectation”: to the contrary, we’re well on the way towards the “Plateau of Productivity”

The Internet of Things…Machine Learning…Self-Driving Cars…Artificial Intelligence…Big Data…Smart Cities…Decision Intelligence…my friends talk to me about their excitement about a whole lot of trends.  But which ones are real, and which will fizzle?

We have Smith using videos and web sites to support his grass roots approach to reforming our systems and we have Pratt’s big systems approach to analyzing the big problems that split the political spectrum. These approaches and, possibly others like them, give me hope that solutions are out there waiting to be found and implemented.

Looking back on the presidential election of 2012, one view of the Obama win is to attribute it to his team’s understanding of a phase shift in electoral dynamics: Democrats looked at historical turnout numbers and perceived a systemic change; in contrast many believed that Republican certainty in a Romney win was based on a reasonably expected regression to the mean.  This is the essential idea behind “data from the future“.   We ignore these principles in this system, as in many others, at our peril.

In light of this history, it’s worth asking if the fundamental dynamics of how elections are won is shifting this year again.

"...we were careful and diligent, writing thorough specifications documents, then carefully crafted designs, and only then could code begin.

All good software engineers know this is the way to go.   Or at least that’s what we always thought.

But it turns out we were wrong.  And not in a small way."

Many companies are facing the prospect of steep increases in the cost of energy in the coming years.  In response, many are looking at alternative energy sources. However, navigating the transition to this new world contains hidden dangers, so an evidence-based modeling approach can make a big difference.  This article looks at this decision-making process…

"The goal of World Makers is to encourage people to build computer simulations of the world. This includes simulating water, weather, crops, land use policy or anything else. Models can be regional or global, simple sketches or full blown simulations.

The classic game ‘Sim City’ by Will Wright is perhaps the best known example of a computer simulation. It lets people build their own imaginary city from the ground up, placing roads, homes and services and measuring their success against the happiness of the population. The goal here is similar - but real - with real data, real stakeholders and real outcomes.

Simulations are a powerful holistic way of understanding and communicating the complexity of the challenges we are facing. The fact is that we are all stakeholders in a complex natural planetary ecosystem. It defines us, our lives, our values. Without nature humanity as we know it will quickly cease to exist.

At the same time we are making poor decisions that are undermining our diverse heritage. We risk runaway effects that may be beyond our ability to control. We are playing a high stakes game of planetary Jenga.

This is a crisis of organization. We routinely spend trillions of dollars of value to make millions of dollars in profit. Consider even just dredging the Louisiana swamplands or cutting the mangrove trees and the impact on New Orleans. Nature simply doesn’t care about our rhetoric or our aspirations. We cannot predict or model outcomes in such a way as to reach consensus. Almost universally the side-effects of our actions dominate over intended consequences. Private interests can pursue short term gains and externalize true costs onto the rest of us. In sum the world has become too complex to understand with current tools.

We simply do not know the outcomes of our decisions. We don’t even have trustworthy predictions of those outcomes. Will the world as we know it even exist after 2100? Or will we see massive ecosystem collapse, human migration, war and other disruptive effects? Or will everything be fine? There is no consensus.

The one phenomena moving as fast as the environmental crisis is the Internet and computing power. The hybrid synthesis of human intuition and brute force computation lets us explore more complex situations in more depth. The simulations we are starting to build will eventually allow the most vulnerable and concerned stakeholders predict outcomes at the intersection of policy, law and land-use in a rigorous manner. By doing so we’re shifting the civic debate from “rhetoric” to “model based” reasoning.

In the future there will be nobody who is not an environmentalist. There will be nobody who isn’t a direct participant in helping save this planet. We will find ourselves needing to get the participation and buy in from millions of voices that are currently disempowered. We will need to find a way to foster diversity. We need to find a way to stop private interests from eating the public good.

Ultimately we’re going to need new models of governance and decision making. It’s clear we’re going to have to hack the systems around us. The structures that got us into this mess are not the same as the structures that are going to get us out. This is how we’re going to save the world."